Found insideThis book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. Generative Adversarial Networks (GANs) are the state of the art neural network models for image generation, but the use of GANs for video generation is still largely unexplored. Found insideThis book constitutes the proceedings of the XVIIIth International Conference of the Italian Association for Artificial Intelligence, AI*IA 2019, held in Rende, Italy, in November 2019. Found inside – Page iGreen Building Management and Smart Automation is an essential scholarly publication that provides an in-depth analysis of design technologies for green building and highlights the smart automation technologies that help in energy ... Found inside – Page iBenefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and ... Found insideThis book is filled with best practices/tips after every project to help you optimize your deep learning models with ease. Best Book on GAN Ê DESCRIPTIONÊ This book aims at simplifying GAN for everyone. This book is very important for machine learning engineers, researchers, students, professors, and professionals. Found insideThis Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. Found insideThis book presents the implementation of 7 practical, real-world projects that will teach you how to leverage TensorFlow Lite and Core ML to perform efficient machine learning on a cross-platform mobile OS. You will get to work on image, ... Found insideVariational AutoEncoders (VAEs) are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of ... The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. Found insideThis book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. Content Description. #Includes bibliographical references and index. Found inside – Page iFeaturing research on topics such as image modeling, remote sensing, and support vector machines, this book is ideally designed for IT specialists, scientists, researchers, engineers, developers, practitioners, industry professionals, ... Found insideThis proceedings book presents selected papers from the 4th Conference on Signal and Information Processing, Networking and Computers (ICSINC) held in Qingdao, China on May 23–25, 2018. Found insideDeep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision. Found inside – Page iiThis is the first handbook on image engineering, and provides a well-structured, comprehensive overview of this new discipline. It also offers detailed information on the various image techniques. Found insideThis book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. This text is imagined as a primer for readers interested in creative applications of AI technologies. Ideally, readers will explore the strategies of this emerging field as outlined, and remix them to suit their desires. Found insideThis book will explore deep learning and generative models, and their applications in artificial intelligence. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. Found insideThe six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in ... Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable ... Found insideThis practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision. Found insideAuthor Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. This book provides a comprehensive introduction to multiple-point geostatistics, where spatial continuity is described using training images. Miller argues that computers can already be as creative as humans--and someday will surpass us. But this is not a dystopian account; Miller celebrates the creative possibilities of artificial intelligence in art, music, and literature. Found insideThe book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. This beginner-friendly guide will give you hands-on experience: learning PyTorch basics developing your first PyTorch neural network exploring neural network refinements to improve performance introduce CUDA GPU acceleration It will ... This two volume set LNCS 8634 and LNCS 8635 constitutes the refereed conference proceedings of the 39th International Symposium on Mathematical Foundations of Computer Science, MFCS 2014, held in Budapest, Hungary, in August 2014. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Found insideThis book constitutes the refereed proceedings of the 21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018, held in Tokyo, Japan, in October/November 2018. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. With the ever-growing volume of visual data as well as revolutionary advances in deep learning (DL) for computer vision, incorporating the visual aspects of the data (e.g., color, fashion styles) yields more relevant suggestions in contrast ... Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Found insideIn this book, we will use different complexities of datasets in order to build end-to-end projects. Found insideThis book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image ... Build image generation and semi-supervised models using Generative Adversarial NetworksAbout This Book* Understand the buzz surrounding Generative Adversarial Networks and how they work, in the simplest manner possible* Develop generative ... Describes ways to incorporate domain modeling into software development. Found insideStep-by-step tutorials on generative adversarial networks in python for image synthesis and image translation. Found insideThis book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial ... Found insideThis book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. What you 'll learn Become familiar with the tools, infrastructure, remix... Ideally, readers will explore the strategies of this new discipline understand how model! 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